diff --git a/python-package/xgboost/dask.py b/python-package/xgboost/dask.py
index c87929292..5fa37dedd 100644
--- a/python-package/xgboost/dask.py
+++ b/python-package/xgboost/dask.py
@@ -42,7 +42,7 @@ from .core import _deprecate_positional_args
 from .training import train as worker_train
 from .tracker import RabitTracker, get_host_ip
 from .sklearn import XGBModel, XGBClassifier, XGBRegressorBase, XGBClassifierBase
-from .sklearn import _wrap_evaluation_matrices, _objective_decorator
+from .sklearn import _wrap_evaluation_matrices, _objective_decorator, _check_rf_callback
 from .sklearn import XGBRankerMixIn
 from .sklearn import xgboost_model_doc
 from .sklearn import _cls_predict_proba
@@ -1710,7 +1710,7 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
         callbacks: Optional[List[TrainingCallback]] = None,
     ) -> "DaskXGBRegressor":
         _assert_dask_support()
-        args = {k: v for k, v in locals().items() if k != "self"}
+        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
         return self._client_sync(self._fit_async, **args)
 
 
@@ -1813,7 +1813,7 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
         callbacks: Optional[List[TrainingCallback]] = None
     ) -> "DaskXGBClassifier":
         _assert_dask_support()
-        args = {k: v for k, v in locals().items() if k != 'self'}
+        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
         return self._client_sync(self._fit_async, **args)
 
     async def _predict_proba_async(
@@ -2001,7 +2001,7 @@ class DaskXGBRanker(DaskScikitLearnBase, XGBRankerMixIn):
         callbacks: Optional[List[TrainingCallback]] = None
     ) -> "DaskXGBRanker":
         _assert_dask_support()
-        args = {k: v for k, v in locals().items() if k != "self"}
+        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
         return self._client_sync(self._fit_async, **args)
 
     # FIXME(trivialfis): arguments differ due to additional parameters like group and qid.
@@ -2047,6 +2047,30 @@ class DaskXGBRFRegressor(DaskXGBRegressor):
     def get_num_boosting_rounds(self) -> int:
         return 1
 
+    # pylint: disable=unused-argument
+    def fit(
+        self,
+        X: _DaskCollection,
+        y: _DaskCollection,
+        *,
+        sample_weight: Optional[_DaskCollection] = None,
+        base_margin: Optional[_DaskCollection] = None,
+        eval_set: Optional[List[Tuple[_DaskCollection, _DaskCollection]]] = None,
+        eval_metric: Optional[Union[str, List[str], Metric]] = None,
+        early_stopping_rounds: Optional[int] = None,
+        verbose: bool = True,
+        xgb_model: Optional[Union[Booster, XGBModel]] = None,
+        sample_weight_eval_set: Optional[List[_DaskCollection]] = None,
+        base_margin_eval_set: Optional[List[_DaskCollection]] = None,
+        feature_weights: Optional[_DaskCollection] = None,
+        callbacks: Optional[List[TrainingCallback]] = None
+    ) -> "DaskXGBRFRegressor":
+        _assert_dask_support()
+        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
+        _check_rf_callback(early_stopping_rounds, callbacks)
+        super().fit(**args)
+        return self
+
 
 @xgboost_model_doc(
     """Implementation of the Scikit-Learn API for XGBoost Random Forest Classifier.
@@ -2086,3 +2110,27 @@ class DaskXGBRFClassifier(DaskXGBClassifier):
 
     def get_num_boosting_rounds(self) -> int:
         return 1
+
+    # pylint: disable=unused-argument
+    def fit(
+        self,
+        X: _DaskCollection,
+        y: _DaskCollection,
+        *,
+        sample_weight: Optional[_DaskCollection] = None,
+        base_margin: Optional[_DaskCollection] = None,
+        eval_set: Optional[List[Tuple[_DaskCollection, _DaskCollection]]] = None,
+        eval_metric: Optional[Union[str, List[str], Metric]] = None,
+        early_stopping_rounds: Optional[int] = None,
+        verbose: bool = True,
+        xgb_model: Optional[Union[Booster, XGBModel]] = None,
+        sample_weight_eval_set: Optional[List[_DaskCollection]] = None,
+        base_margin_eval_set: Optional[List[_DaskCollection]] = None,
+        feature_weights: Optional[_DaskCollection] = None,
+        callbacks: Optional[List[TrainingCallback]] = None
+    ) -> "DaskXGBRFClassifier":
+        _assert_dask_support()
+        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
+        _check_rf_callback(early_stopping_rounds, callbacks)
+        super().fit(**args)
+        return self
diff --git a/python-package/xgboost/sklearn.py b/python-package/xgboost/sklearn.py
index 0d441cb94..573da3ae8 100644
--- a/python-package/xgboost/sklearn.py
+++ b/python-package/xgboost/sklearn.py
@@ -40,6 +40,17 @@ class XGBRankerMixIn:  # pylint: disable=too-few-public-methods
     _estimator_type = "ranker"
 
 
+def _check_rf_callback(
+    early_stopping_rounds: Optional[int],
+    callbacks: Optional[List[TrainingCallback]],
+) -> None:
+    if early_stopping_rounds is not None or callbacks is not None:
+        raise NotImplementedError(
+            "`early_stopping_rounds` and `callbacks` are not implemented for"
+            " random forest."
+        )
+
+
 _SklObjective = Optional[
     Union[
         str, Callable[[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]
@@ -1420,6 +1431,30 @@ class XGBRFClassifier(XGBClassifier):
     def get_num_boosting_rounds(self) -> int:
         return 1
 
+    # pylint: disable=unused-argument
+    @_deprecate_positional_args
+    def fit(
+        self,
+        X: array_like,
+        y: array_like,
+        *,
+        sample_weight: Optional[array_like] = None,
+        base_margin: Optional[array_like] = None,
+        eval_set: Optional[List[Tuple[array_like, array_like]]] = None,
+        eval_metric: Optional[Union[str, List[str], Metric]] = None,
+        early_stopping_rounds: Optional[int] = None,
+        verbose: Optional[bool] = True,
+        xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
+        sample_weight_eval_set: Optional[List[array_like]] = None,
+        base_margin_eval_set: Optional[List[array_like]] = None,
+        feature_weights: Optional[array_like] = None,
+        callbacks: Optional[List[TrainingCallback]] = None
+    ) -> "XGBRFClassifier":
+        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
+        _check_rf_callback(early_stopping_rounds, callbacks)
+        super().fit(**args)
+        return self
+
 
 @xgboost_model_doc(
     "Implementation of the scikit-learn API for XGBoost regression.",
@@ -1451,18 +1486,46 @@ class XGBRFRegressor(XGBRegressor):
         reg_lambda: float = 1e-5,
         **kwargs: Any
     ) -> None:
-        super().__init__(learning_rate=learning_rate, subsample=subsample,
-                         colsample_bynode=colsample_bynode,
-                         reg_lambda=reg_lambda, **kwargs)
+        super().__init__(
+            learning_rate=learning_rate,
+            subsample=subsample,
+            colsample_bynode=colsample_bynode,
+            reg_lambda=reg_lambda,
+            **kwargs
+        )
 
     def get_xgb_params(self) -> Dict[str, Any]:
         params = super().get_xgb_params()
-        params['num_parallel_tree'] = self.n_estimators
+        params["num_parallel_tree"] = self.n_estimators
         return params
 
     def get_num_boosting_rounds(self) -> int:
         return 1
 
+    # pylint: disable=unused-argument
+    @_deprecate_positional_args
+    def fit(
+        self,
+        X: array_like,
+        y: array_like,
+        *,
+        sample_weight: Optional[array_like] = None,
+        base_margin: Optional[array_like] = None,
+        eval_set: Optional[List[Tuple[array_like, array_like]]] = None,
+        eval_metric: Optional[Union[str, List[str], Metric]] = None,
+        early_stopping_rounds: Optional[int] = None,
+        verbose: Optional[bool] = True,
+        xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
+        sample_weight_eval_set: Optional[List[array_like]] = None,
+        base_margin_eval_set: Optional[List[array_like]] = None,
+        feature_weights: Optional[array_like] = None,
+        callbacks: Optional[List[TrainingCallback]] = None
+    ) -> "XGBRFRegressor":
+        args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
+        _check_rf_callback(early_stopping_rounds, callbacks)
+        super().fit(**args)
+        return self
+
 
 @xgboost_model_doc(
     'Implementation of the Scikit-Learn API for XGBoost Ranking.',
diff --git a/tests/python/test_with_sklearn.py b/tests/python/test_with_sklearn.py
index afc1d857c..75020eec1 100644
--- a/tests/python/test_with_sklearn.py
+++ b/tests/python/test_with_sklearn.py
@@ -402,6 +402,10 @@ def run_boston_housing_rf_regression(tree_method):
         labels = y[test_index]
         assert mean_squared_error(preds, labels) < 35
 
+    rfreg = xgb.XGBRFRegressor()
+    with pytest.raises(NotImplementedError):
+        rfreg.fit(X, y, early_stopping_rounds=10)
+
 
 def test_boston_housing_rf_regression():
     run_boston_housing_rf_regression("hist")